Klasifikasi Text Dokumen Web Berbasis Supervised Learning Sebagai Pemodelan Aplikasi Pembelajaran Kebudayaan Melayu di Indonesia - Mustakim, Mustakim; Salisah, Febi Nur; Suryani, Suryani (Building of Informatics, Technology and Science (BITS), 2025)
Indonesia, as the largest archipelagic country, is home to diverse cultures, including Malay culture in Riau Province. The website features numerous text documents, including articles, news, and personal documents, uploaded by members of the cultural community. This study aims to support the preservation of Malay culture through technology by implementing a digital learning system based on Machine Learning. Previous research has identified weaknesses in the application of intelligent systems and machine learning algorithms. This study tests five classification algorithms Random Forest, SVM, Naïve Bayes, KNN, and PNN to improve the system's accuracy and performance. The results show that Random Forest achieved the highest accuracy of 91.17%, followed by KNN at 88.23%, SVM and NBC at 82.35%, and PNN at 76.47%. The developed Digital Learning System (DLS) received positive feedback, with a User Acceptance Test (UAT) score of 86% and a 100% success rate in Blackbox testing, demonstrating stable performance across various devices. This research introduces a new innovation in Malay cultural preservation applications, utilizing Machine Learning algorithms to enhance both accuracy and functionality.